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Limitations of Viterbi decoding Number of states may be too large –Beam search: at each time step, maintain a short list of the most probable words and only extend transitions from those words into the next time step Words with multiple pronunciation variants may get a smaller probability than incorrect words with fewer pronunciation paths Word model for “tomato”

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Limitations of Viterbi decoding Number of states may be too large Beam search: at each time step, maintain a short list of the most probable words and only extend transitions from those words into the next time step Words with multiple pronunciation variants may get a smaller probability than incorrect words with fewer pronunciation paths –Use the forward algorithm instead of Viterbi algorithm The Markov assumption is too weak to capture the constraints of real language

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Advanced techniques Multiple pass decoding –Let the Viterbi decoder return multiple candidate utterances and then re-rank them using a more sophisticated language model, e.g., n-gram model A* decoding –Build a search tree whose nodes are words and whose paths are possible utterances –Path cost is given by the likelihood of the acoustic features given the words inferred so far –Heuristic function estimates the best-scoring extension until the end of the utterance